import pandas as pd
import numpy as np
import pydotplus
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
%matplotlib inline
from scipy.stats import zscore
from io import StringIO
from efficient_apriori import apriori
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn import preprocessing as spp
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report,roc_curve,auc
from sklearn.tree import export_graphviz
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from IPython.display import Image
print("libraries imported!")

libraries imported!

dataset = pd.read_csv('HR_comma_sep.csv', 
                         sep= ',')
data = dataset.copy(deep=True)

data.head(10)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14999 entries, 0 to 14998
Data columns (total 10 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   satisfaction_level     14999 non-null  float64
 1   last_evaluation        14999 non-null  float64
 2   number_project         14999 non-null  int64  
 3   average_montly_hours   14999 non-null  int64  
 4   time_spend_company     14999 non-null  int64  
 5   Work_accident          14999 non-null  int64  
 6   promotion_last_5years  14999 non-null  int64  
 7   departmennt            14999 non-null  object 
 8   salary                 14999 non-null  object 
 9   left                   14999 non-null  int64  
dtypes: float64(2), int64(6), object(2)
memory usage: 1.1+ MB

Getting Rid of inconsistencies¶

#The column "department" is named as "departmennt". so lets rename it to "department".
data.rename(columns={'departmennt': 'department'}, inplace=True)

#Salary has two different names for same category like "low" and "Low", making them same
def salary(row):
    if row['salary'] == 'High' or row['salary'] == 'high':
        return 'high'
    elif row['salary'] == 'Medium' or row['salary'] == 'medium':
        return 'medium'
    else:
        return 'low'
data['salary'] = data.apply(salary,axis=1)

data.head()

data.tail()

Finding missing values and replacing them by an appropriate strategy.¶

(Back-fill, mode-fill)¶

data['satisfaction_level'] = data['satisfaction_level'].fillna(data['satisfaction_level'].mode()[0])
data['last_evaluation'] = data['last_evaluation'].fillna(data['last_evaluation'].mode()[0])
data['number_project'] = data['number_project'].fillna(data['number_project'].mode()[0])
data['average_montly_hours'] = data['average_montly_hours'].fillna(int(data['average_montly_hours'].mode()[0]))
data['time_spend_company'] = data['time_spend_company'].fillna(data['time_spend_company'].mode()[0])
data['Work_accident'] = data['Work_accident'].bfill()
data['promotion_last_5years'] = data['promotion_last_5years'].bfill()
data['department'] = data['department'].bfill()
data['salary'] = data['salary'].bfill()
data['left'] = data['left'].bfill()

#In the above table, the salary column contains strings(high, medium, low). 
#We can add another column('salary_numerical') to the table that will return a number if salary is high(3), medium(2) or low(1).
#and check the describe command.

def salary(row):
    if row['salary'] == 'high':
        return 3
    elif row['salary'] == 'medium':
        return 2
    else:
        return 1
    
data['salary_numerical'] = data.apply(salary, axis=1)

data.describe()

#normalizig for better visualization

normalized_average_montly_hours = data['average_montly_hours']/data['average_montly_hours'].mean()
normalized_average_montly_hours = data['average_montly_hours']/data['average_montly_hours'].mean()
normalized_time_spend_company = data['time_spend_company']/data['time_spend_company'].mean()
normalized_number_project = data['number_project']/data['number_project'].mean()

fig, ax = plt.subplots()
ax.boxplot((data['satisfaction_level'],data['last_evaluation'],normalized_number_project,normalized_average_montly_hours,normalized_time_spend_company,data['Work_accident'],data['promotion_last_5years'],data['left']), vert=False, showmeans=True, meanline=True,
           labels=('satisfaction_level','last_evaluation','number_project','average_montly_hours','time_spend_company','Work_accident','promotion_last_5years','left'), patch_artist=True,
           medianprops={'linewidth': 2, 'color': 'purple'},
           meanprops={'linewidth': 2, 'color': 'red'})
plt.show()

Now we find what is correlated in the dataset.¶

sns.set(style="white")

corr = data.corr()

mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

vf, ax = plt.subplots(figsize=(11, 9))

sns.heatmap(corr, mask=mask, vmax=.3, center=0,annot=True,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})

<matplotlib.axes._subplots.AxesSubplot at 0x2bc37645100>

1:¶

This correlation matrix tells us that the higher someone is satisfied in their job the less likely they are to leave.

2:¶

Time spent with the company is positively correlated with leaving,

3:¶

suggesting the longer someone works for this organization the more likely they are too leave.

4:¶

Another relationship that makes sense is that a higher salary appears to be negatively associated with leaving.

5:¶

One correlation in the above matrix doesn't quite make sense.

Having had a work accident is negatively related to leaving the company, which doesn't really make intuitive sense.On the surface this doesn't make a whole lot of sense because you would assume that an employee felt unsafe at an organization that they might want to leave.

left = data.groupby('left')
left.mean()

Here you can interpret, Employees who left the company had low satisfaction level, low promotion rate, low salary, and worked more compare to who stayed in the company.

left_count=data.groupby('left').count()
plt.bar(left_count.index.values, left_count['satisfaction_level'])
plt.xlabel('Employees Left Company')
plt.ylabel('Number of Employees')
plt.show()

data.left.value_counts()

0    11428
1     3571
Name: left, dtype: int64

Here, you can see out of 15,000 approx 3,571 were left, and 11,428 stayed. The no of employee left is 23 % of the total employment.

num_projects=data.groupby('number_project').count()
plt.bar(num_projects.index.values, num_projects['satisfaction_level'])
plt.xlabel('Number of Projects')
plt.ylabel('Number of Employees')
plt.show()

Most of the employee is doing the project from 3-5

time_spent=data.groupby('time_spend_company').count()
plt.bar(time_spent.index.values, time_spent['satisfaction_level'])
plt.xlabel('Number of Years Spend in Company')
plt.ylabel('Number of Employees')
plt.show()

Most of the employee experience between 2-4 years. Also, there is a massive gap between 3 years and 4 years experienced employee.

features=['number_project','time_spend_company','Work_accident','left', 'promotion_last_5years','department','salary']
fig=plt.subplots(figsize=(10,15))
for i, j in enumerate(features):
    plt.subplot(4, 2, i+1)
    plt.subplots_adjust(hspace = 1.0)
    sns.countplot(x=j,data = data)
    plt.xticks(rotation=90)
    plt.title("No. of employee")

You can observe the following points in the above visualization:

1 Most of the employee is doing the project from 3-5.

2 There is a huge drop between 3 years and 4 years experienced employee.

3 The no of employee left is 23 % of the total employment.

4 A decidedly less number of employee get the promotion in the last 5 year.

5 The sales department is having maximum no.of employee followed by technical and support

6 Most of the employees are getting salary either medium or low.

fig=plt.subplots(figsize=(10,15))
for i, j in enumerate(features):
    plt.subplot(4, 2, i+1)
    plt.subplots_adjust(hspace = 1.0)
    sns.countplot(x=j,data = data, hue='left')
    plt.xticks(rotation=90)
    plt.title("No. of employee")

You can observe the following points in the above visualization:

1 Those employees who have the number of projects more than 5 were left the company.

2 The employee who had done 6 and 7 projects, left the company it seems to like that they were overloaded with work.

3 The employee with five-year experience is leaving more because of no promotions in last 5 years and more than 6 years experience are not leaving because of affection with the company.

4 Those who promotion in last 5 years they didn't leave, i.e., all those left they didn't get the promotion in the previous 5 years.

Data Analysis and Visualization Summary:¶

Following features are most influencing a person to leave the company:

Promotions: Employees are far more likely to quit their job if they haven't received a promotion in the last 5 years.
Time with Company: Here, The three-year mark looks like a time to be a crucial point in an employee's career. Most of them quit their job around the three-year mark. Another important point is 6-years point, where the employee is very unlikely to leave.
Number Of Projects: Employee engagement is another critical factor to influence the employee to leave the company. Employees with 3-5 projects are less likely to leave the company. The employee with less and more number of projects are likely to leave.
Salary: Most of the employees that quit among the mid or low salary groups.

What makes people leave the company¶

Department and People left¶

data_department = data.groupby(by='department',as_index=False).mean()
#Pure Count
data_department.plot(kind = "bar", x ='department', y='left', color = 'red')

<matplotlib.axes._subplots.AxesSubplot at 0x2bc3b1956a0>

Findings:-¶

1:¶

We See that hr, accounting and Technical Departments have more employees who left the company.

2:¶

However, without knowing the total staffing count at the organization, this variable isn't too helpful.

Satisfaction & People left¶

#Creating a new column and assigning qualitative rankings for three(3) Satisfaction levels(High, Medium and Low).
#and adding a new column "Satisfaction" to hold the qualitative rankings.

def satisfaction(row):
    if row['satisfaction_level'] >= .5:
        return 'medium'
    elif .3 <= row['satisfaction_level'] < .5:
        return 'high'
    else:
        return 'low'

data['qualitative_satisfaction'] = data.apply(satisfaction, axis=1)

data_satisfaction = data.groupby(by='qualitative_satisfaction',as_index=False).mean()
data_satisfaction.head()

#Pure Count
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='left', color = 'red')

<matplotlib.axes._subplots.AxesSubplot at 0x2bc3b404730>

#Percentage or Rate.
data_satisfaction = data.groupby(by=['qualitative_satisfaction'])
left_rate_satisfaction = data_satisfaction['left'].sum() / data_satisfaction['left'].count()
ax = left_rate_satisfaction.plot(kind='barh')

Findings:-¶

1:¶

It is interesting. Those with high satisfaction levels are more likely to leave based on percentage of those who left, which doesn't make intuitive sense

2:¶

if we use our three-category breakdown. This chart suggests that those who have medium satisfaction are the least likely to leave.

Salary and People left¶

#befor that let's group data by salary
data_salary = data.groupby(by=['salary'],as_index=False).count()
data_salary

#Pure Count
data_salary.plot(kind = "bar", x ='salary', y='left', color = 'red')

<matplotlib.axes._subplots.AxesSubplot at 0x2bc3b489430>

#Percentage or Rate.
data_salary = data.groupby(by=['salary'])
left_rate_salary = data_salary['left'].sum() / data_salary['left'].count()
ax = left_rate_salary.plot(kind='barh')

Findings:-¶

1:¶

Salary have a linear relationship with leaving, with the more money you earn the less likely you are to leave.

Collective Effect of Salary and Satisfaction in terms of Leaving the Company¶

# Set a default value
data_salary_satisfaction = data.groupby(by=['salary','qualitative_satisfaction'])
# data_salary_satisfaction.head()
left_rate_sal_sat = data_salary_satisfaction['left'].sum() / data_salary_satisfaction['left'].count()
ax = left_rate_sal_sat.plot(kind='barh', color='purple')

Findings:-¶

1:¶

Employees with the highest rate of leaving earns a low salary and reports a medium satisfaction rate.

2:¶

Aso Employees with the lowest rate of leaving earns the highest salary and reports medium satsifaction.

Now, let's have a closer look at the satisfaction level variable¶

ax = sns.distplot(data['satisfaction_level'])

Finding:-¶

Satisfaction is not normally distributed.

Reasons behind higher and lower Satisfaction¶

data_satisfaction = data.groupby(by='qualitative_satisfaction',as_index=False).mean()
# Satisfaction & Project Numbers
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='number_project', color = 'blue')
# Satisfaction & average_montly_hours
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='average_montly_hours', color = 'blue')
# Satisfaction & time_spend_company
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='time_spend_company', color = 'blue')
# Satisfaction & Work_accident
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='Work_accident', color = 'blue')
# Satisfaction & promotion_last_5years
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='promotion_last_5years', color = 'blue')
# Satisfaction & salary_numerical
data_satisfaction.plot(kind = "bar", x ='qualitative_satisfaction', y='salary_numerical', color = 'blue')

<matplotlib.axes._subplots.AxesSubplot at 0x2bc37ca19d0>

Findings:-¶

1:¶

This shows people with lowest number of projects and lowest number of time spend time monthly have highest satisfaction and people with most number of projects and highest monthly hours tends to loose internal peace.

2:¶

Total time spent in company does't seem to have much effect on satisfactin, but people who spend 4 or more years tends to have low satisfactio, i.e passing time effects thier satisfaction.

3:¶

People who have most number of work accidents have medium satisfaction, but less number od work accidents doesn't promise more satisfaction

4:¶

Similarly promotion in last five years doesn't have linear effect on satisfaction.

5:¶

Salary doesn't seem to have any effect on satisfaction, as people with high, medium or low satisfaction have same average salary. This is quite strange as because salary is believed to have a major effect on employees satisfaction.

Cluster Analysis¶

#data processing
dep_sal_backup = data[['department','salary']]
le = LabelEncoder()
k = le.fit_transform(data['department'])
data['department_numerical'] = k
data.drop([ 'salary', 'department', 'qualitative_satisfaction'], axis=1, inplace=True)
data.head()

KMeans¶

X = data.to_numpy()
# Normalized numpy array, used later in the visualization step

distortions = []
K = range(1,10)
for k in K:
    kmeanModel = KMeans(n_clusters=k, random_state = 0)
    kmeanModel.fit(X)
    distortions.append(kmeanModel.inertia_)

plt.figure(figsize=(8,6))
plt.plot(K, distortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Distortion')
plt.title('The Elbow Method showing the optimal n_clusters')
plt.show()

Findings:¶

Elbow method suggests optimal number of clusters is 3

# Initialize
kmeans = KMeans(n_clusters=3, random_state = 0)
# Fit
kmeans.fit(X)
# Print labels
print(kmeans.labels_)

[2 1 1 ... 2 1 2]

kmeans_data = data.copy(deep = True)
kmeans_data['labels'] = kmeans.labels_
kmeans_data.head()

kmeans_columns = kmeans_data.columns  
print(type(kmeans_columns))
# remove labels
kmeans_columns = kmeans_columns.delete(10)
# print(kmeans_columns)
for col in kmeans_columns:
    data_age = kmeans_data.groupby(by='labels',as_index=False).mean()
    data_age.plot(kind = "bar", x ='labels', y=col, color = 'red')

<class 'pandas.core.indexes.base.Index'>

Characteristics for each cluster¶

Lowest rating at Last Evaluation, Least number of projects, Lowest Average Monthly hours
Highest rating at Last Evaluation , Most number of projects, Highest Average Monthly Hours, Highest left rate
Highest Satisfaction, Most work accidents, Highest promotion, Lowest left rate

All three clusters have almost equal salary, department distribution, time spend company

Outlier Analysis¶

Outlier detection can be done on satisfaction_level, last_evaluation, number_project, average_montly_hours, time_spend_company because the rest of the attributes are boolean or categorical

outlier_data = data.drop([ 'Work_accident', 'promotion_last_5years', 'left', 'salary_numerical', 'department_numerical'], axis='columns')
outlier_data.head()

BoxPlot¶

fig, ax = plt.subplots()
ax.boxplot((outlier_data['satisfaction_level'], outlier_data['last_evaluation']), vert=False, showmeans=True, meanline=True,
           labels=('satisfaction_level','last_evaluation'), patch_artist=True,
           medianprops={'linewidth': 4, 'color': 'purple'},
           meanprops={'linewidth': 4, 'color': 'red'})
plt.show()
norm_average_montly_hours = outlier_data['average_montly_hours']/outlier_data['average_montly_hours'].mean()
norm_number_project = outlier_data['number_project']/outlier_data['number_project'].mean()
norm_time_spend_company = outlier_data['time_spend_company']/outlier_data['time_spend_company'].mean()
fig, ax = plt.subplots()
ax.boxplot((norm_number_project,norm_time_spend_company ,norm_average_montly_hours ), vert=False, showmeans=True, meanline=True,
           labels=('number_project','norm_time_spend_company','average_monthly_hours'), patch_artist=True,
           medianprops={'linewidth': 4, 'color': 'purple'},
           meanprops={'linewidth': 4, 'color': 'red'})
plt.show()

Findings:¶

ZScore¶

zscore_data = data.drop([ 'Work_accident', 'promotion_last_5years', 'left', 'salary_numerical', 'department_numerical'], axis='columns')
zscore_data.head()

score = np.abs(zscore(zscore_data))
threshold = 3
zoutliers = np.where(score > threshold)
# type(outliers)
zoutliers_in_dataset = pd.DataFrame(zoutliers).transpose()
zoutliers_in_dataset

Each row refrences to a particular datapoint in zscore_data. The first element contains the row number and second element has column number, which mean zscore_data[11007][4] have a Z-score higher than 3.

Findings:¶

IQR¶

iqr_data = data.drop([ 'Work_accident', 'promotion_last_5years', 'left', 'salary_numerical', 'department_numerical'], axis='columns')
iqr_data.head()

for col in iqr_data.columns:
    Q1, Q3 = np.percentile(iqr_data[col], 25), np.percentile(iqr_data[col], 75)
    IQR, k = (Q3 - Q1), 3
    lower_Outlier, higher_Outlier = Q1 - (k * IQR), Q3 + (k * IQR) 
    # identify outliers
    iqr_outliers = [j for j in iqr_data[col] if j < lower_Outlier or j > higher_Outlier]
    print("\n" + col + "\n")
    print(iqr_outliers)

satisfaction_level

[]

last_evaluation

[]

number_project

[]

average_montly_hours

[]

time_spend_company

[8, 8, 8, 8, 8, 10, 8, 8, 8, 8, 8, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 8, 8, 8, 8, 8, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 10, 10, 10, 8, 8, 8, 10, 10, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 10, 10, 10, 10]

Findings¶

Prediction Algorithms (which employ will leave)¶

X=data.drop(['left'],axis=1)
Y=data['left']

X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3,)

features = list(X_test.columns)
X_test

Y_test

13481    0
12664    1
9232     0
3444     0
4393     0
        ..
10848    0
13898    0
3988     0
5915     0
5321     0
Name: left, Length: 4500, dtype: int64

Logistical Regrassion¶

lr = LogisticRegression(solver = 'lbfgs', max_iter = 400)
lr.fit(X_train, Y_train)
predict = lr.predict(X_train)
print("Logistic Regression Training Score is",lr.score(X_train,Y_train))
print("Logistic Regression Testing Score is",lr.score(X_test,Y_test))

Logistic Regression Training Score is 0.7905514810934374
Logistic Regression Testing Score is 0.7953333333333333

accuracy_lr = lr.score(X_test,Y_test)*100
print("Accuracy of Logistic Regression is ",accuracy_lr)

Accuracy of Logistic Regression is  79.53333333333333

for col,value in zip(data[features].columns,lr.coef_[0]):
    print(col,"*",value,"+")
print(lr.intercept_[0])

satisfaction_level * -4.011294686307858 +
last_evaluation * 0.6276738724356047 +
number_project * -0.3155459632341092 +
average_montly_hours * 0.004516103912669045 +
time_spend_company * 0.2661973976727499 +
Work_accident * -1.5674158921110983 +
promotion_last_5years * -1.4683386138815178 +
salary_numerical * -0.6753455426169653 +
department_numerical * 0.03250113948178719 +
0.9543423557433329

print("Confusion Matrix for logistic Regression")
print(confusion_matrix(Y_test, lr.predict(X_test)))

Confusion Matrix for logistic Regression
[[3184  249]
 [ 672  395]]

print("Classification Report for Logistic Regression")
print(classification_report(Y_test, lr.predict(X_test)))

Classification Report for Logistic Regression
              precision    recall  f1-score   support

           0       0.83      0.93      0.87      3433
           1       0.61      0.37      0.46      1067

    accuracy                           0.80      4500
   macro avg       0.72      0.65      0.67      4500
weighted avg       0.78      0.80      0.78      4500

As observed our Logistic Regression has an accuracy of approx 78% for its predictions and on first glance this might seem to be a pretty good performing model. But,this model has a lower Specificity.

# Compute ROC curve and ROC area for each class
y_score = lr.decision_function(data[features])
n_classes =2
fpr = dict()
tpr = dict()
roc_auc = dict()
fpr, tpr, _ = roc_curve(data['left'], y_score)
roc_auc = auc(fpr, tpr)

plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', lw=lw,\
         label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()

Findings:

DT¶

dt=DecisionTreeClassifier(criterion = 'entropy')

dt.fit(X_train,Y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',
                       max_depth=None, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

prediction_dt=dt.predict(X_test)
accuracy_dt=accuracy_score(Y_test,prediction_dt)*100
print('Accuracy = ' + str(accuracy_dt))
prediction_dt

Accuracy = 97.53333333333333

array([0, 1, 0, ..., 0, 0, 0], dtype=int64)

Y_test

13481    0
12664    1
9232     0
3444     0
4393     0
        ..
10848    0
13898    0
3988     0
5915     0
5321     0
Name: left, Length: 4500, dtype: int64

print("Confusion Matrix for Decision Tree")
print(confusion_matrix(Y_test, prediction_dt))

Confusion Matrix for Decision Tree
[[3372   61]
 [  50 1017]]

print("Classification Report for Decision Tree")
print(classification_report(Y_test, prediction_dt))

Classification Report for Decision Tree
              precision    recall  f1-score   support

           0       0.99      0.98      0.98      3433
           1       0.94      0.95      0.95      1067

    accuracy                           0.98      4500
   macro avg       0.96      0.97      0.97      4500
weighted avg       0.98      0.98      0.98      4500

#Decision Tree Plot
export_graphviz(dt,feature_names=features, out_file="dtree.dot",
               filled = True, rounded = True,
               special_characters = True)

dot_data = StringIO()
export_graphviz(dt, feature_names = features, out_file=dot_data,
               filled = True, rounded = True,
               special_characters= True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())

Feature Importance¶

feature_importance=pd.DataFrame(dt.feature_importances_,index=X_train.columns,columns=['Importance']).sort_values('Importance',ascending=False)
feature_importance

#checkin our model for anyother data
catagory = ['Employe will stay', 'Employ will leave']
custom_dt = [[10,200,4,0,0, 0.80, 0.96, 2, 4]]
catagory[int(dt.predict(custom_dt))]

'Employe will stay'

KNN¶

sc=StandardScaler().fit(X_train)
X_train_std=sc.transform(X_train)
X_test_std=sc.transform(X_test)

X_train_std

array([[ 0.38932548, -1.60426742, -1.45758472, ..., -0.148322  ,
        -0.93859253,  0.3859842 ],
       [ 0.26807222,  0.44320421,  0.17153989, ..., -0.148322  ,
        -0.93859253, -0.31561693],
       [ 1.44018708, -0.96077634,  0.98610219, ..., -0.148322  ,
         2.19024792,  0.3859842 ],
       ...,
       [-0.66153611, -1.37027066, -1.45758472, ..., -0.148322  ,
        -0.93859253,  1.08758532],
       [-0.4998651 ,  0.44320421,  0.17153989, ..., -0.148322  ,
         2.19024792,  0.73678476],
       [ 0.51057874,  1.43769044,  0.17153989, ..., -0.148322  ,
        -0.93859253,  1.08758532]])

k_range=range(1,26)
scores={}
scores_list=[]


for k in k_range:
    knn=KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train_std,Y_train)
    prediction_knn=knn.predict(X_test_std)
    scores[k]=accuracy_score(Y_test,prediction_knn)*100
    scores_list.append(accuracy_score(Y_test,prediction_knn))

scores

{1: 96.91111111111111,
 2: 96.55555555555554,
 3: 95.86666666666666,
 4: 96.0,
 5: 95.57777777777777,
 6: 95.97777777777777,
 7: 95.66666666666667,
 8: 95.71111111111111,
 9: 95.51111111111112,
 10: 95.62222222222222,
 11: 95.39999999999999,
 12: 95.44444444444444,
 13: 95.19999999999999,
 14: 95.11111111111111,
 15: 94.93333333333334,
 16: 95.06666666666666,
 17: 94.73333333333333,
 18: 94.82222222222222,
 19: 94.71111111111111,
 20: 94.73333333333333,
 21: 94.55555555555556,
 22: 94.57777777777778,
 23: 94.46666666666667,
 24: 94.55555555555556,
 25: 94.39999999999999}

plt.plot(k_range,scores_list)

[<matplotlib.lines.Line2D at 0x2bc3f563130>]

#using n_neighbors = 1, as it has the highest accuracy
knn=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train_std,Y_train)
prediction_knn=knn.predict(X_test_std)
accuracy_knn=accuracy_score(Y_test,prediction_knn)*100
print('Accuracy = ' + str(accuracy_knn))
prediction_knn

Accuracy = 96.91111111111111

array([0, 1, 0, ..., 0, 0, 0], dtype=int64)

Y_test

13481    0
12664    1
9232     0
3444     0
4393     0
        ..
10848    0
13898    0
3988     0
5915     0
5321     0
Name: left, Length: 4500, dtype: int64

#checkin our model for anyother data
X_knn=np.array([[20,500,10,6,0,0.10,0.30,1,8]])
X_knn_std=sc.transform(X_knn)
X_knn_prediction=knn.predict(X_knn_std)
catagory[X_knn_prediction[0]]

'Employe will stay'

RANDOM FOREST¶

rf = RandomForestClassifier(n_estimators=35,criterion="entropy")
rf.fit(X_train,Y_train)
pred_rf = rf.predict(X_test)
print("Training accuracy for Random Forest is",rf.score(X_train,Y_train)*100)
print("Testing accuracy for Random Forest is",rf.score(X_test,Y_test)*100)
accuracy_rf = rf.score(X_test,Y_test)*100

Training accuracy for Random Forest is 99.98095056672064
Testing accuracy for Random Forest is 98.6888888888889

print("Confusion Matrix of Random Forest \n",confusion_matrix(Y_test,pred_rf))

Confusion Matrix of Random Forest 
 [[3421   12]
 [  47 1020]]

print("Classification report for Random Forest")
print(classification_report(Y_test,pred_rf))

Classification report for Random Forest
              precision    recall  f1-score   support

           0       0.99      1.00      0.99      3433
           1       0.99      0.96      0.97      1067

    accuracy                           0.99      4500
   macro avg       0.99      0.98      0.98      4500
weighted avg       0.99      0.99      0.99      4500

Feature Importance according to Random Forest¶

feature_importances = pd.DataFrame(rf.feature_importances_,
                                   index = features,
                                    columns=['importance']).sort_values('importance',ascending=False)
feature_importances

Clearly some features like satisfaction_level, time_spend_company has a higher impact on the retention of the employees. People having a higher satisfaction are more likely to stay in the organization.

Accuracy Comparison DT vs KNN vs Logistical Regression vs Random Forest¶

algorithms=['Logi. Regression', 'Decision Tree','KNN', 'Random Forest']
scores=[accuracy_lr, accuracy_dt,accuracy_knn,  accuracy_rf]
plt.xlabel("Algorithms")
plt.ylabel("Accuracy Score")
sns.barplot(algorithms,scores)
plt.show()

Finding Interesting Patterns¶

Apriori¶

def data_generator(filename):
  """
  Data generator, needs to return a generator to be called several times.
  Use this approach if data is too large to fit in memory. If not use a list.
  """
  def data_gen():
    with open(filename) as file:
      for line in file:
        yield tuple(k.strip() for k in line.split(','))      

  return data_gen

transactions = data_generator('HR_comma_sep.csv')
itemsets, rules = apriori(transactions, min_support=0.3, min_confidence=0.3)
rules

[{1} -> {0},
 {0} -> {1},
 {2} -> {0},
 {0} -> {2},
 {3} -> {0},
 {0} -> {3},
 {low} -> {0},
 {0} -> {low},
 {medium} -> {0},
 {0} -> {medium},
 {4} -> {0},
 {0} -> {4}]

itemsets

{1: {('2',): 5408,
  ('low',): 7316,
  ('3',): 8716,
  ('0',): 14995,
  ('1',): 5983,
  ('medium',): 6446,
  ('4',): 6345},
 2: {('0', '1'): 5979,
  ('0', '2'): 5405,
  ('0', '3'): 8713,
  ('0', 'low'): 7313,
  ('0', 'medium'): 6445,
  ('0', '4'): 6344}}

PCA Analysis¶

pca = PCA(n_components = 2)

pca.fit(data)

PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)

pca.explained_variance_ratio_

array([0.99505374, 0.00328353])

pca.explained_variance_

array([2494.61707963,    8.23187396])

pca.get_covariance

<bound method _BasePCA.get_covariance of PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)>

pca.mean_

array([6.12833522e-01, 7.16101740e-01, 3.80305354e+00, 2.01050337e+02,
       3.49823322e+00, 1.44609641e-01, 2.12680845e-02, 2.38082539e-01,
       1.59470631e+00, 5.87052470e+00])

pca.score_samples(data)

array([-14.88019135, -18.41685095, -19.42760601, ..., -14.9821359 ,
       -16.0049539 , -15.08192061])

pca.singular_values_

array([6116.72027809,  351.3710939 ])

pca.get_precision

<bound method _BasePCA.get_precision of PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)>

Principal Component Analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Naive Bayes Classifier¶

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of particular feature in a class is unrelated to the presence of any other feature. The model was trained using the training datasets generated previously ie., X_train and Y_train with an accuracy of 79.89% (approximately) while accuracy at testing was 80.13% (approximately). Guassian is used in classification and assumes that feature follow a normal distribution due to which the accuracy is not at par with other methods. It can be observed from the figure that 2111 points were mislabeled out of 104099 which makes up 14.07% (approximately) of the dataset.

gnb = GaussianNB()

gnb.fit(X_train, Y_train)

GaussianNB(priors=None, var_smoothing=1e-09)

pred_gnb = gnb.predict(X_train)
gnb.score(X_train, Y_train)

0.786932088770359

accuracy_gnb = gnb.score(X_test, Y_test)
accuracy_gnb

0.7944444444444444

gnb.predict_proba(X_test)

array([[1.00000000e+00, 1.97867215e-34],
       [1.94385259e-02, 9.80561474e-01],
       [9.37526631e-01, 6.24733694e-02],
       ...,
       [9.99938127e-01, 6.18731158e-05],
       [7.11320728e-01, 2.88679272e-01],
       [7.04988224e-01, 2.95011776e-01]])

print("Number of mislabeled points out of total %d points : %d" %(X_train.shape[0], (Y_train != pred_gnb).sum()))

Number of mislabeled points out of total 10499 points : 2237

print("Accuracy = ", accuracy_gnb*100)

Accuracy =  79.44444444444444

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	promotion_last_5years	left	salary_numerical
count	14999.000000	14999.000000	14999.000000	14999.000000	14999.000000	14999.000000	14999.000000	14999.000000	14999.000000
mean	0.612834	0.716102	3.803054	201.050337	3.498233	0.144610	0.021268	0.238083	1.594706
std	0.248631	0.171169	1.232592	49.943099	1.460136	0.351719	0.144281	0.425924	0.637183
min	0.090000	0.360000	2.000000	96.000000	2.000000	0.000000	0.000000	0.000000	1.000000
25%	0.440000	0.560000	3.000000	156.000000	3.000000	0.000000	0.000000	0.000000	1.000000
50%	0.640000	0.720000	4.000000	200.000000	3.000000	0.000000	0.000000	0.000000	2.000000
75%	0.820000	0.870000	5.000000	245.000000	4.000000	0.000000	0.000000	0.000000	2.000000
max	1.000000	1.000000	7.000000	310.000000	10.000000	1.000000	1.000000	1.000000	3.000000

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	promotion_last_5years	salary_numerical
left
0	0.666810	0.715473	3.786664	199.060203	3.380032	0.175009	0.026251	1.650945
1	0.440098	0.718113	3.855503	207.419210	3.876505	0.047326	0.005321	1.414730

	qualitative_satisfaction	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	promotion_last_5years	left	salary_numerical
0	high	0.411455	0.573588	2.703469	161.674375	3.271913	0.096979	0.015666	0.597538	1.515479
1	low	0.152213	0.781651	5.291272	236.805468	4.288644	0.117245	0.012093	0.496845	1.538906
2	medium	0.748778	0.740814	3.814324	204.656394	3.412154	0.161866	0.024386	0.098310	1.625288

	0	1
0	11007	4
1	11008	4
2	11009	4
3	11010	4
4	11011	4
...	...	...
371	14205	4
372	14206	4
373	14207	4
374	14208	4
375	14209	4

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	departmennt	salary	left
0	0.38	0.53	2	157	3	sales	low	1
1	0.80	0.86	5	262	6	sales	medium	1
2	0.11	0.88	7	272	4	sales	medium	1
3	0.72	0.87	5	223	5	sales	low	1
4	0.37	0.52	2	159	3	sales	low	1
5	0.41	0.50	2	153	3	sales	low	1
6	0.10	0.77	6	247	4	sales	low	1
7	0.92	0.85	5	259	5	sales	low	1
8	0.89	1.00	5	224	5	sales	low	1
9	0.42	0.53	2	142	3	sales	low	1

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	department	salary	left
14994	0.40	0.57	2	151	3	support	low	1
14995	0.37	0.48	2	160	3	support	low	1
14996	0.37	0.53	2	143	3	support	low	1
14997	0.11	0.96	6	280	4	support	low	1
14998	0.37	0.52	2	158	3	support	low	1

	salary	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	promotion_last_5years	department	left	salary_numerical	qualitative_satisfaction
0	high	1237	1237	1237	1237	1237	1237	1237	1237	1237	1237	1237
1	low	7316	7316	7316	7316	7316	7316	7316	7316	7316	7316	7316
2	medium	6446	6446	6446	6446	6446	6446	6446	6446	6446	6446	6446

	satisfaction_level	last_evaluation	number_project	average_montly_hours	time_spend_company	Work_accident	promotion_last_5years	salary_numerical	department_numerical
13481	0.16	0.93	5	210	7	0	1	2	4
12664	0.10	0.77	5	276	4	0	0	1	8
9232	0.88	0.50	3	206	2	0	0	2	1
3444	0.96	0.75	5	176	4	0	0	1	7
4393	0.94	0.47	5	131	6	0	0	2	9
...	...	...	...	...	...	...	...	...	...
10848	0.83	0.86	4	98	4	0	0	1	7
13898	0.71	0.78	4	227	2	0	0	1	8
3988	0.72	0.51	3	146	2	1	0	1	7
5915	0.91	0.57	3	158	3	0	0	1	9
5321	0.83	0.61	5	246	3	0	0	2	8

	Importance
satisfaction_level	0.430498
time_spend_company	0.205259
last_evaluation	0.142603
average_montly_hours	0.103868
number_project	0.092846
department_numerical	0.013946
salary_numerical	0.008674
Work_accident	0.001954
promotion_last_5years	0.000351

	importance
satisfaction_level	0.322432
time_spend_company	0.220783
average_montly_hours	0.149466
last_evaluation	0.135482
number_project	0.134283
department_numerical	0.018297
salary_numerical	0.012050
Work_accident	0.006350
promotion_last_5years	0.000856

Getting Rid of inconsistencies¶

Finding missing values and replacing them by an appropriate strategy.¶

(Back-fill, mode-fill)¶

Now we find what is correlated in the dataset.¶

1:¶

2:¶

3:¶

4:¶

5:¶

6:¶

Data Analysis and Visualization Summary:¶

What makes people leave the company¶

Department and People left¶

Findings:-¶

1:¶

2:¶

Satisfaction & People left¶

Findings:-¶

1:¶

2:¶

Salary and People left¶

Findings:-¶

1:¶

Collective Effect of Salary and Satisfaction in terms of Leaving the Company¶

Findings:-¶

1:¶

2:¶

Now, let's have a closer look at the satisfaction level variable¶

Finding:-¶

Reasons behind higher and lower Satisfaction¶

Findings:-¶

1:¶

2:¶

3:¶

4:¶

5:¶

Cluster Analysis¶

KMeans¶

Findings:¶

Characteristics for each cluster¶

Outlier Analysis¶

BoxPlot¶

Findings:¶

ZScore¶

Findings:¶

IQR¶

Findings¶

Prediction Algorithms (which employ will leave)¶

Logistical Regrassion¶

DT¶

Feature Importance¶

KNN¶

RANDOM FOREST¶

Feature Importance according to Random Forest¶

Accuracy Comparison DT vs KNN vs Logistical Regression vs Random Forest¶

Finding Interesting Patterns¶

Apriori¶

PCA Analysis¶

Naive Bayes Classifier¶